{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\mjoshi2\Box\2024 Research\Colombia Data & Research\Research\Gender Research\GenderAnalysis\Data\PSJ Data\PSJ Stipulation.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}26 Nov 2024, 11:02:32
{txt}
{com}. //Figure 2. Coefficient plot.
. set seed 122475
{txt}
{com}. xtologit imp_  point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-59493.487}  
Iteration 2:{space 3}log likelihood = {res:-59493.268}  
Iteration 3:{space 3}log likelihood = {res:-59493.268}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-41675.539}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-41675.539}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34286.432}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33727.724}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33477.995}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33397.765}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33379.522}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33378.928}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33379.016}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33379.015}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}5{txt}){col 67}={col 70}{res}    20.62
{txt}Log pseudolikelihood  = {res}-33379.015{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0010

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
point1_Gender {c |}{col 15}{res}{space 2}-2.157341{col 27}{space 2} .5507926{col 38}{space 1}   -3.92{col 47}{space 3}0.000{col 55}{space 4}-3.236875{col 68}{space 3}-1.077807
{txt}point2_Gender {c |}{col 15}{res}{space 2}-1.520746{col 27}{space 2} .8922779{col 38}{space 1}   -1.70{col 47}{space 3}0.088{col 55}{space 4}-3.269579{col 68}{space 3} .2280864
{txt}point3_Gender {c |}{col 15}{res}{space 2} .6653635{col 27}{space 2} .8846271{col 38}{space 1}    0.75{col 47}{space 3}0.452{col 55}{space 4}-1.068474{col 68}{space 3} 2.399201
{txt}point4_Gender {c |}{col 15}{res}{space 2}  -.88527{col 27}{space 2} .4872007{col 38}{space 1}   -1.82{col 47}{space 3}0.069{col 55}{space 4}-1.840166{col 68}{space 3} .0696258
{txt}point5_Gender {c |}{col 15}{res}{space 2} .5656847{col 27}{space 2} 1.193319{col 38}{space 1}    0.47{col 47}{space 3}0.635{col 55}{space 4}-1.773178{col 68}{space 3} 2.904548
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.400584{col 27}{space 2} .2276221{col 55}{space 4}-2.846715{col 68}{space 3}-1.954452
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .6094824{col 27}{space 2} .2430945{col 55}{space 4} .1330259{col 68}{space 3} 1.085939
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 2.824898{col 27}{space 2} .2771288{col 55}{space 4} 2.281736{col 68}{space 3} 3.368061
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 19.80385{col 27}{space 2} 1.978789{col 55}{space 4} 16.28164{col 68}{space 3} 24.08802
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m3
{txt}
{com}. xtologit gender_imp_recode point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res: -58654.49}  
Iteration 2:{space 3}log likelihood = {res: -58653.14}  
Iteration 3:{space 3}log likelihood = {res: -58653.14}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -41244.37}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -41244.37}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34311.612}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33744.363}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33501.358}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33424.664}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33405.052}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33404.551}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33404.642}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33404.643}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}5{txt}){col 67}={col 70}{res}    43.63
{txt}Log pseudolikelihood  = {res}-33404.643{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}point1_Gender {c |}{col 19}{res}{space 2}-2.944859{col 31}{space 2} .5510595{col 42}{space 1}   -5.34{col 51}{space 3}0.000{col 59}{space 4}-4.024916{col 72}{space 3}-1.864802
{txt}{space 4}point2_Gender {c |}{col 19}{res}{space 2}-2.872815{col 31}{space 2} .8139975{col 42}{space 1}   -3.53{col 51}{space 3}0.000{col 59}{space 4}-4.468221{col 72}{space 3} -1.27741
{txt}{space 4}point3_Gender {c |}{col 19}{res}{space 2} -1.88702{col 31}{space 2} .8964486{col 42}{space 1}   -2.10{col 51}{space 3}0.035{col 59}{space 4}-3.644027{col 72}{space 3} -.130013
{txt}{space 4}point4_Gender {c |}{col 19}{res}{space 2}-2.113591{col 31}{space 2}  .760328{col 42}{space 1}   -2.78{col 51}{space 3}0.005{col 59}{space 4}-3.603807{col 72}{space 3}-.6233754
{txt}{space 4}point5_Gender {c |}{col 19}{res}{space 2} -.058739{col 31}{space 2} 1.112615{col 42}{space 1}   -0.05{col 51}{space 3}0.958{col 59}{space 4}-2.239423{col 72}{space 3} 2.121945
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-2.344243{col 31}{space 2} .2241576{col 59}{space 4}-2.783583{col 72}{space 3}-1.904902
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} .5907385{col 31}{space 2} .2398468{col 59}{space 4} .1206475{col 72}{space 3}  1.06083
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} 2.790325{col 31}{space 2} .2742881{col 59}{space 4}  2.25273{col 72}{space 3} 3.327919
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 19.47422{col 31}{space 2}  1.97598{col 59}{space 4} 15.96217{col 72}{space 3} 23.75902
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m4
{txt}
{com}. 
. coefplot (m3,  label(Non-gender perspective)) (m4,  label(Gender perspective)) ///
>         ||, drop(_cons) xline(0)   xscale(range(-3 2))
{res}{txt}
{com}.         
. 
. //Appendix Tables
. //Table 2a. Descriptive Statistics for figures 2 and 3 in the manuscript
. //Summary Stat
. sum imp_  gender_imp_recode gender_binary gender_imp point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender point6_Gender

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}imp_ {c |}{res}     44,511    1.342679     1.13517          0          3
{txt}gender_imp~e {c |}{res}     44,511    1.280155    1.131864          0          3
{txt}gender_bin~y {c |}{res}     44,511    .2248882    .4175134          0          1
{txt}{space 2}gender_imp {c |}{res}     10,010    .8877123    .9197921          0          3
{txt}point1_Gen~r {c |}{res}     44,511    .0674665    .2508309          0          1
{txt}{hline 13}{c +}{hline 57}
point2_Gen~r {c |}{res}     44,511    .0536272    .2252831          0          1
{txt}point3_Gen~r {c |}{res}     44,511    .0415178    .1994868          0          1
{txt}point4_Gen~r {c |}{res}     44,511    .0328683    .1782938          0          1
{txt}point5_Gen~r {c |}{res}     44,511     .019029    .1366284          0          1
{txt}point6_Gen~r {c |}{res}     44,511    .0103795    .1013507          0          1
{txt}
{com}. sum imp_  if gender_binary==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}imp_ {c |}{res}     34,501    1.394018    1.161779          0          3
{txt}
{com}. sum imp_  if gender_binary==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}imp_ {c |}{res}     10,010    1.165734    1.018661          0          3
{txt}
{com}. 
. //Appendix Table 3a. results for coefficient plot in the manuscript
. xtologit imp_  point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-59493.487}  
Iteration 2:{space 3}log likelihood = {res:-59493.268}  
Iteration 3:{space 3}log likelihood = {res:-59493.268}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-41675.539}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-41675.539}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34286.432}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33727.724}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33477.995}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33397.765}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33379.522}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33378.928}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33379.016}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33379.015}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}5{txt}){col 67}={col 70}{res}    20.62
{txt}Log pseudolikelihood  = {res}-33379.015{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0010

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
point1_Gender {c |}{col 15}{res}{space 2}-2.157341{col 27}{space 2} .5507926{col 38}{space 1}   -3.92{col 47}{space 3}0.000{col 55}{space 4}-3.236875{col 68}{space 3}-1.077807
{txt}point2_Gender {c |}{col 15}{res}{space 2}-1.520746{col 27}{space 2} .8922779{col 38}{space 1}   -1.70{col 47}{space 3}0.088{col 55}{space 4}-3.269579{col 68}{space 3} .2280864
{txt}point3_Gender {c |}{col 15}{res}{space 2} .6653635{col 27}{space 2} .8846271{col 38}{space 1}    0.75{col 47}{space 3}0.452{col 55}{space 4}-1.068474{col 68}{space 3} 2.399201
{txt}point4_Gender {c |}{col 15}{res}{space 2}  -.88527{col 27}{space 2} .4872007{col 38}{space 1}   -1.82{col 47}{space 3}0.069{col 55}{space 4}-1.840166{col 68}{space 3} .0696258
{txt}point5_Gender {c |}{col 15}{res}{space 2} .5656847{col 27}{space 2} 1.193319{col 38}{space 1}    0.47{col 47}{space 3}0.635{col 55}{space 4}-1.773178{col 68}{space 3} 2.904548
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.400584{col 27}{space 2} .2276221{col 55}{space 4}-2.846715{col 68}{space 3}-1.954452
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .6094824{col 27}{space 2} .2430945{col 55}{space 4} .1330259{col 68}{space 3} 1.085939
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 2.824898{col 27}{space 2} .2771288{col 55}{space 4} 2.281736{col 68}{space 3} 3.368061
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 19.80385{col 27}{space 2} 1.978789{col 55}{space 4} 16.28164{col 68}{space 3} 24.08802
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit gender_imp_recode point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res: -58654.49}  
Iteration 2:{space 3}log likelihood = {res: -58653.14}  
Iteration 3:{space 3}log likelihood = {res: -58653.14}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -41244.37}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -41244.37}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34311.612}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33744.363}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33501.358}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33424.664}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33405.052}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33404.551}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33404.642}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33404.643}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}5{txt}){col 67}={col 70}{res}    43.63
{txt}Log pseudolikelihood  = {res}-33404.643{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}point1_Gender {c |}{col 19}{res}{space 2}-2.944859{col 31}{space 2} .5510595{col 42}{space 1}   -5.34{col 51}{space 3}0.000{col 59}{space 4}-4.024916{col 72}{space 3}-1.864802
{txt}{space 4}point2_Gender {c |}{col 19}{res}{space 2}-2.872815{col 31}{space 2} .8139975{col 42}{space 1}   -3.53{col 51}{space 3}0.000{col 59}{space 4}-4.468221{col 72}{space 3} -1.27741
{txt}{space 4}point3_Gender {c |}{col 19}{res}{space 2} -1.88702{col 31}{space 2} .8964486{col 42}{space 1}   -2.10{col 51}{space 3}0.035{col 59}{space 4}-3.644027{col 72}{space 3} -.130013
{txt}{space 4}point4_Gender {c |}{col 19}{res}{space 2}-2.113591{col 31}{space 2}  .760328{col 42}{space 1}   -2.78{col 51}{space 3}0.005{col 59}{space 4}-3.603807{col 72}{space 3}-.6233754
{txt}{space 4}point5_Gender {c |}{col 19}{res}{space 2} -.058739{col 31}{space 2} 1.112615{col 42}{space 1}   -0.05{col 51}{space 3}0.958{col 59}{space 4}-2.239423{col 72}{space 3} 2.121945
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-2.344243{col 31}{space 2} .2241576{col 59}{space 4}-2.783583{col 72}{space 3}-1.904902
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} .5907385{col 31}{space 2} .2398468{col 59}{space 4} .1206475{col 72}{space 3}  1.06083
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} 2.790325{col 31}{space 2} .2742881{col 59}{space 4}  2.25273{col 72}{space 3} 3.327919
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 19.47422{col 31}{space 2}  1.97598{col 59}{space 4} 15.96217{col 72}{space 3} 23.75902
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Data for Appendix figure 1
. sum imp_ if gender_binary==0

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}imp_ {c |}{res}     34,501    1.394018    1.161779          0          3
{txt}
{com}. sum imp_ if gender_binary==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 8}imp_ {c |}{res}     10,010    1.165734    1.018661          0          3
{txt}
{com}. sum gender_imp_recode if gender_binary==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
gender_imp~e {c |}{res}     10,010    .8877123    .9197921          0          3
{txt}
{com}. 
. 
. //Appendix Table 5a. Student t-test on implementation status by gender and non-gender provisions
. ttest imp_, by(gender_binary)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12} 34,501{col 22} 1.394018{col 34} .0062547{col 46} 1.161779{col 58} 1.381758{col 70} 1.406277
       {txt}1 {c |}{res}{col 12} 10,010{col 22} 1.165734{col 34} .0101815{col 46} 1.018661{col 58} 1.145776{col 70} 1.185692
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 44,511{col 22} 1.342679{col 34} .0053806{col 46}  1.13517{col 58} 1.332133{col 70} 1.353225
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2282833{col 34} .0128419{col 58} .2031129{col 70} .2534537
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} 17.7764
{txt}Ho: diff = 0                                     degrees of freedom = {res}   44509

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. //Appendix Table 5b. Student t-test on implementation status of gender and non-gender provisions with gender perspectives for gender provisions
. 
. ttest gender_imp_recode, by(gender_binary)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12} 34,501{col 22} 1.394018{col 34} .0062547{col 46} 1.161779{col 58} 1.381758{col 70} 1.406277
       {txt}1 {c |}{res}{col 12} 10,010{col 22} .8877123{col 34} .0091933{col 46} .9197921{col 58} .8696915{col 70} .9057331
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 44,511{col 22} 1.280155{col 34} .0053649{col 46} 1.131864{col 58}  1.26964{col 70} 1.290671
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .5063053{col 34} .0126238{col 58} .4815624{col 70} .5310482
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} 40.1072
{txt}Ho: diff = 0                                     degrees of freedom = {res}   44509

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. //Appendix Table 5c. Model 1 Gender stipulation
. xtologit imp_  gender_binary, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-59733.378}  
Iteration 2:{space 3}log likelihood = {res:-59733.352}  
Iteration 3:{space 3}log likelihood = {res:-59733.352}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-41797.587}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-41797.587}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34296.471}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33738.082}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33481.828}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33396.766}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33383.611}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33383.172}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33383.221}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33383.219}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}1{txt}){col 67}={col 70}{res}     4.26
{txt}Log pseudolikelihood  = {res}-33383.219{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0389

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.8668898{col 27}{space 2} .4198225{col 38}{space 1}   -2.06{col 47}{space 3}0.039{col 55}{space 4}-1.689727{col 68}{space 3}-.0440529
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.374371{col 27}{space 2} .2301111{col 55}{space 4}-2.825381{col 68}{space 3}-1.923362
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .6356653{col 27}{space 2} .2456763{col 55}{space 4} .1541486{col 68}{space 3} 1.117182
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} 2.850858{col 27}{space 2} .2794495{col 55}{space 4} 2.303147{col 68}{space 3} 3.398569
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 20.14575{col 27}{space 2} 2.020716{col 55}{space 4} 16.55022{col 68}{space 3}  24.5224
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m1 
{txt}
{com}. margins, dydx(gender_binary) 
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    44,506
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:gender_binary}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(0.imp_), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(1.imp_), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(2.imp_), predict(pr outcome(2))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._predict}:{space 1}{res:Pr(3.imp_), predict(pr outcome(3))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27} Delta-method
{col 15}{c |}      dy/dx{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}gender_binary {txt}{c |}
{space 5}_predict {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .0741786{col 27}{space 2}  .034096{col 38}{space 1}    2.18{col 47}{space 3}0.030{col 55}{space 4} .0073517{col 68}{space 3} .1410055
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.0080208{col 27}{space 2} .0040565{col 38}{space 1}   -1.98{col 47}{space 3}0.048{col 55}{space 4}-.0159713{col 68}{space 3}-.0000703
{txt}{space 11}3  {c |}{col 15}{res}{space 2}-.0047411{col 27}{space 2} .0066261{col 38}{space 1}   -0.72{col 47}{space 3}0.474{col 55}{space 4}-.0177281{col 68}{space 3} .0082458
{txt}{space 11}4  {c |}{col 15}{res}{space 2}-.0614167{col 27}{space 2} .0308204{col 38}{space 1}   -1.99{col 47}{space 3}0.046{col 55}{space 4}-.1218235{col 68}{space 3}-.0010099
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0) recastci(rarea) 
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: _outcome{p_end}
{res}{txt}
{com}. 
. //Model 2 Gender adjusted imp coding
. xtologit gender_imp_recode gender_binary, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res:-58883.279}  
Iteration 2:{space 3}log likelihood = {res: -58882.44}  
Iteration 3:{space 3}log likelihood = {res:-58882.439}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-41346.776}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-41346.776}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34318.921}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33753.133}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33506.698}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33427.932}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33408.518}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33407.954}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33408.053}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33408.053}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}1{txt}){col 67}={col 70}{res}    27.21
{txt}Log pseudolikelihood  = {res}-33408.053{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-2.176724{col 31}{space 2} .4172983{col 42}{space 1}   -5.22{col 51}{space 3}0.000{col 59}{space 4}-2.994614{col 72}{space 3}-1.358835
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-2.329229{col 31}{space 2} .2269578{col 59}{space 4}-2.774058{col 72}{space 3}  -1.8844
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}  .605657{col 31}{space 2} .2424358{col 59}{space 4} .1304916{col 72}{space 3} 1.080822
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} 2.805041{col 31}{space 2}  .276341{col 59}{space 4} 2.263422{col 72}{space 3} 3.346659
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 19.77539{col 31}{space 2} 2.000331{col 59}{space 4}   16.219{col 72}{space 3} 24.11162
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m2
{txt}
{com}. margins, dydx(gender_binary)
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    44,506
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:gender_binary}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._predict}:{space 1}{res:Pr(0.gender_imp_recode), predict(pr outcome(0))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._predict}:{space 1}{res:Pr(1.gender_imp_recode), predict(pr outcome(1))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._predict}:{space 1}{res:Pr(2.gender_imp_recode), predict(pr outcome(2))}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._predict}:{space 1}{res:Pr(3.gender_imp_recode), predict(pr outcome(3))}{p_end}
{p2colreset}{...}

{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27} Delta-method
{col 15}{c |}      dy/dx{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}gender_binary {txt}{c |}
{space 5}_predict {c |}
{space 11}1  {c |}{col 15}{res}{space 2} .1791642{col 27}{space 2} .0309284{col 38}{space 1}    5.79{col 47}{space 3}0.000{col 55}{space 4} .1185456{col 68}{space 3} .2397827
{txt}{space 11}2  {c |}{col 15}{res}{space 2}-.0168056{col 27}{space 2} .0093136{col 38}{space 1}   -1.80{col 47}{space 3}0.071{col 55}{space 4}  -.03506{col 68}{space 3} .0014488
{txt}{space 11}3  {c |}{col 15}{res}{space 2}-.0116818{col 27}{space 2} .0139557{col 38}{space 1}   -0.84{col 47}{space 3}0.403{col 55}{space 4}-.0390345{col 68}{space 3} .0156709
{txt}{space 11}4  {c |}{col 15}{res}{space 2}-.1506768{col 27}{space 2} .0344631{col 38}{space 1}   -4.37{col 47}{space 3}0.000{col 55}{space 4}-.2182233{col 68}{space 3}-.0831303
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, yline(0) recastci(rarea) 
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: _outcome{p_end}
{res}{txt}
{com}. 
. coefplot (m1,  label(Non-gender perspective)) (m2,  label(Gender perspective)) ///
>         ||, drop(_cons) xline(0)   xscale(range(-3 2))
{res}{txt}
{com}. 
. graph save Graph "C:\Users\mjoshi2\Box\2024 Research\Colombia Data & Research\Research\Gender Research\GenderAnalysis\Data\Bivariate_coef_plot.gph", replace
{res}{txt}(file C:\Users\mjoshi2\Box\2024 Research\Colombia Data & Research\Research\Gender Research\GenderAnalysis\Data\Bivariate_coef_plot.gph saved)

{com}. 
. //Appendix Table 6a. Bivariate analysis with the last month of the data
. ologit imp_ gender_binary if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-757.18294}  
Iteration 2:{space 3}log pseudolikelihood = {res:-757.18058}  
Iteration 3:{space 3}log pseudolikelihood = {res:-757.18058}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.88
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0050
{txt}Log pseudolikelihood = {res}-757.18058{txt}{col 49}Pseudo R2{col 67}= {res}    0.0046

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2} -.470064{col 27}{space 2} .1674701{col 38}{space 1}   -2.81{col 47}{space 3}0.005{col 55}{space 4}-.7982993{col 68}{space 3}-.1418288
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.045792{col 27}{space 2} .1410446{col 55}{space 4}-2.322235{col 68}{space 3} -1.76935
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-.1471208{col 27}{space 2} .0951033{col 55}{space 4}-.3335199{col 68}{space 3} .0392783
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .7081219{col 27}{space 2} .0988994{col 55}{space 4} .5142825{col 68}{space 3} .9019612
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode gender_binary if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-754.03386}  
Iteration 2:{space 3}log pseudolikelihood = {res:-754.00076}  
Iteration 3:{space 3}log pseudolikelihood = {res:-754.00075}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     27.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-754.00075{txt}{col 49}Pseudo R2{col 67}= {res}    0.0154

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.8689207{col 31}{space 2}  .166189{col 42}{space 1}   -5.23{col 51}{space 3}0.000{col 59}{space 4}-1.194645{col 72}{space 3}-.5431963
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2} -2.05902{col 31}{space 2} .1408686{col 59}{space 4}-2.335118{col 72}{space 3}-1.782923
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-.1577433{col 31}{space 2} .0960602{col 59}{space 4}-.3460177{col 72}{space 3} .0305312
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .7250057{col 31}{space 2} .1003705{col 59}{space 4} .5282831{col 72}{space 3} .9217282
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Appendix Table 6b. Replicate analysis for coefficient figure 2 with the last month of the data
. 
. ologit imp_  point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-752.61297}  
Iteration 2:{space 3}log pseudolikelihood = {res:-752.59548}  
Iteration 3:{space 3}log pseudolikelihood = {res:-752.59548}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     26.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-752.59548{txt}{col 49}Pseudo R2{col 67}= {res}    0.0106

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
point1_Gender {c |}{col 15}{res}{space 2}-.9777835{col 27}{space 2} .2001636{col 38}{space 1}   -4.88{col 47}{space 3}0.000{col 55}{space 4}-1.370097{col 68}{space 3}-.5854701
{txt}point2_Gender {c |}{col 15}{res}{space 2}-.6506261{col 27}{space 2} .3556845{col 38}{space 1}   -1.83{col 47}{space 3}0.067{col 55}{space 4}-1.347755{col 68}{space 3} .0465028
{txt}point3_Gender {c |}{col 15}{res}{space 2}-.0948921{col 27}{space 2} .3502987{col 38}{space 1}   -0.27{col 47}{space 3}0.786{col 55}{space 4}-.7814651{col 68}{space 3} .5916808
{txt}point4_Gender {c |}{col 15}{res}{space 2}-.3834508{col 27}{space 2} .2723269{col 38}{space 1}   -1.41{col 47}{space 3}0.159{col 55}{space 4}-.9172016{col 68}{space 3} .1503001
{txt}point5_Gender {c |}{col 15}{res}{space 2} .5100553{col 27}{space 2} .7514922{col 38}{space 1}    0.68{col 47}{space 3}0.497{col 55}{space 4}-.9628423{col 68}{space 3} 1.982953
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-2.075153{col 27}{space 2} .1434529{col 55}{space 4}-2.356316{col 68}{space 3} -1.79399
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-.1590436{col 27}{space 2} .0956346{col 55}{space 4} -.346484{col 68}{space 3} .0283967
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .7071732{col 27}{space 2}  .099254{col 55}{space 4}  .512639{col 68}{space 3} .9017074
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode point1_Gender point2_Gender point3_Gender point4_Gender point5_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-749.23856}  
Iteration 2:{space 3}log pseudolikelihood = {res:-749.16239}  
Iteration 3:{space 3}log pseudolikelihood = {res:-749.16236}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     48.30
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-749.16236{txt}{col 49}Pseudo R2{col 67}= {res}    0.0217

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}point1_Gender {c |}{col 19}{res}{space 2}-1.169819{col 31}{space 2} .2004536{col 42}{space 1}   -5.84{col 51}{space 3}0.000{col 59}{space 4}  -1.5627{col 72}{space 3}-.7769369
{txt}{space 4}point2_Gender {c |}{col 19}{res}{space 2} -1.18514{col 31}{space 2}  .303793{col 42}{space 1}   -3.90{col 51}{space 3}0.000{col 59}{space 4}-1.780563{col 72}{space 3}-.5897165
{txt}{space 4}point3_Gender {c |}{col 19}{res}{space 2}-.9151452{col 31}{space 2}  .332458{col 42}{space 1}   -2.75{col 51}{space 3}0.006{col 59}{space 4}-1.566751{col 72}{space 3}-.2635394
{txt}{space 4}point4_Gender {c |}{col 19}{res}{space 2}-.7245294{col 31}{space 2}  .349241{col 42}{space 1}   -2.07{col 51}{space 3}0.038{col 59}{space 4}-1.409029{col 72}{space 3}-.0400295
{txt}{space 4}point5_Gender {c |}{col 19}{res}{space 2} .2901785{col 31}{space 2} .6553189{col 42}{space 1}    0.44{col 51}{space 3}0.658{col 59}{space 4} -.994223{col 72}{space 3}  1.57458
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}  -2.0923{col 31}{space 2} .1437405{col 59}{space 4}-2.374026{col 72}{space 3}-1.810574
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-.1692578{col 31}{space 2} .0966688{col 59}{space 4}-.3587253{col 72}{space 3} .0202096
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .7247646{col 31}{space 2} .1007398{col 59}{space 4} .5273183{col 72}{space 3} .9222109
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Appendix Table 6c. replicate for Figure 2 (Appendix Table 3c with interaction
. 
. xtologit imp_ gender_binary point1 point2  point3 point4  point5 point1_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-56642.113}  
Iteration 2:{space 3}log likelihood = {res:-56616.048}  
Iteration 3:{space 3}log likelihood = {res:-56616.013}  
Iteration 4:{space 3}log likelihood = {res:-56616.013}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -40182.17}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -40182.17}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34137.058}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33585.751}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33416.828}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33347.959}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33337.654}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33337.262}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33337.301}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33337.304}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   105.56
{txt}Log pseudolikelihood  = {res}-33337.304{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2} .0347539{col 27}{space 2}  .486186{col 38}{space 1}    0.07{col 47}{space 3}0.943{col 55}{space 4}-.9181532{col 68}{space 3}  .987661
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-4.095296{col 27}{space 2}  .617157{col 38}{space 1}   -6.64{col 47}{space 3}0.000{col 55}{space 4}-5.304901{col 68}{space 3} -2.88569
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-4.363884{col 27}{space 2} .6631921{col 38}{space 1}   -6.58{col 47}{space 3}0.000{col 55}{space 4}-5.663717{col 68}{space 3}-3.064052
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.3299465{col 27}{space 2} .6389959{col 38}{space 1}   -0.52{col 47}{space 3}0.606{col 55}{space 4}-1.582355{col 68}{space 3} .9224624
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-2.573044{col 27}{space 2}  .588472{col 38}{space 1}   -4.37{col 47}{space 3}0.000{col 55}{space 4}-3.726428{col 68}{space 3} -1.41966
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-3.134772{col 27}{space 2} .6319021{col 38}{space 1}   -4.96{col 47}{space 3}0.000{col 55}{space 4}-4.373278{col 68}{space 3}-1.896267
{txt}point1_Gender {c |}{col 15}{res}{space 2}-.1654541{col 27}{space 2} .7776334{col 38}{space 1}   -0.21{col 47}{space 3}0.832{col 55}{space 4}-1.689587{col 68}{space 3} 1.358679
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.539584{col 27}{space 2} .4912616{col 55}{space 4}-5.502439{col 68}{space 3}-3.576729
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.529125{col 27}{space 2}  .490053{col 55}{space 4}-2.489611{col 68}{space 3}-.5686384
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .6876347{col 27}{space 2} .4982005{col 55}{space 4}-.2888204{col 68}{space 3}  1.66409
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 16.52735{col 27}{space 2} 1.661126{col 55}{space 4} 13.57222{col 68}{space 3} 20.12591
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit imp_ gender_binary point1 point2  point3 point4  point5 point2_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res: -56527.01}  
Iteration 2:{space 3}log likelihood = {res:-56499.436}  
Iteration 3:{space 3}log likelihood = {res:-56499.397}  
Iteration 4:{space 3}log likelihood = {res:-56499.397}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-40127.323}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-40127.323}  
Iteration 1:{space 3}log pseudolikelihood = {res: -34127.97}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33582.201}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33414.718}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33346.631}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33336.525}  
Iteration 6:{space 3}log pseudolikelihood = {res: -33336.15}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33336.187}  
Iteration 8:{space 3}log pseudolikelihood = {res: -33336.19}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   108.25
{txt}Log pseudolikelihood  = {res} -33336.19{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.3408217{col 27}{space 2} .4213444{col 38}{space 1}   -0.81{col 47}{space 3}0.419{col 55}{space 4}-1.166642{col 68}{space 3} .4849982
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-4.041403{col 27}{space 2} .5845018{col 38}{space 1}   -6.91{col 47}{space 3}0.000{col 55}{space 4}-5.187005{col 68}{space 3}  -2.8958
{txt}{space 7}point2 {c |}{col 15}{res}{space 2} -4.76961{col 27}{space 2} .7062334{col 38}{space 1}   -6.75{col 47}{space 3}0.000{col 55}{space 4}-6.153802{col 68}{space 3}-3.385419
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.2913156{col 27}{space 2} .6368851{col 38}{space 1}   -0.46{col 47}{space 3}0.647{col 55}{space 4}-1.539588{col 68}{space 3} .9569563
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-2.490559{col 27}{space 2} .5843086{col 38}{space 1}   -4.26{col 47}{space 3}0.000{col 55}{space 4}-3.635783{col 68}{space 3}-1.345335
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-3.113465{col 27}{space 2} .6321363{col 38}{space 1}   -4.93{col 47}{space 3}0.000{col 55}{space 4}-4.352429{col 68}{space 3}  -1.8745
{txt}point2_Gender {c |}{col 15}{res}{space 2} 1.530766{col 27}{space 2} 1.043048{col 38}{space 1}    1.47{col 47}{space 3}0.142{col 55}{space 4}-.5135711{col 68}{space 3} 3.575103
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.566427{col 27}{space 2} .4901881{col 55}{space 4}-5.527178{col 68}{space 3}-3.605676
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.555889{col 27}{space 2} .4890442{col 55}{space 4}-2.514398{col 68}{space 3}-.5973803
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .6608727{col 27}{space 2} .4972288{col 55}{space 4}-.3136778{col 68}{space 3} 1.635423
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 16.43126{col 27}{space 2} 1.659551{col 55}{space 4} 13.48031{col 68}{space 3}  20.0282
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit imp_ gender_binary point1 point2  point3 point4  point5 point3_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-56468.289}  
Iteration 2:{space 3}log likelihood = {res:-56439.231}  
Iteration 3:{space 3}log likelihood = {res:-56439.188}  
Iteration 4:{space 3}log likelihood = {res:-56439.188}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-40099.974}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-40099.974}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34119.503}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33580.571}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -33413.8}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33346.116}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33336.107}  
Iteration 6:{space 3}log pseudolikelihood = {res: -33335.74}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33335.777}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33335.779}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   107.18
{txt}Log pseudolikelihood  = {res}-33335.779{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2} .3717429{col 27}{space 2} .4231969{col 38}{space 1}    0.88{col 47}{space 3}0.380{col 55}{space 4}-.4577078{col 68}{space 3} 1.201194
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-4.256619{col 27}{space 2} .5845121{col 38}{space 1}   -7.28{col 47}{space 3}0.000{col 55}{space 4}-5.402242{col 68}{space 3}-3.110997
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-4.446777{col 27}{space 2}  .658337{col 38}{space 1}   -6.75{col 47}{space 3}0.000{col 55}{space 4}-5.737094{col 68}{space 3} -3.15646
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.0510034{col 27}{space 2} .6662422{col 38}{space 1}   -0.08{col 47}{space 3}0.939{col 55}{space 4}-1.356814{col 68}{space 3} 1.254807
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-2.645897{col 27}{space 2} .5866174{col 38}{space 1}   -4.51{col 47}{space 3}0.000{col 55}{space 4}-3.795646{col 68}{space 3}-1.496148
{txt}{space 7}point5 {c |}{col 15}{res}{space 2} -3.14942{col 27}{space 2}   .62968{col 38}{space 1}   -5.00{col 47}{space 3}0.000{col 55}{space 4} -4.38357{col 68}{space 3} -1.91527
{txt}point3_Gender {c |}{col 15}{res}{space 2}-1.796442{col 27}{space 2} 1.016812{col 38}{space 1}   -1.77{col 47}{space 3}0.077{col 55}{space 4}-3.789357{col 68}{space 3} .1964721
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.515439{col 27}{space 2} .4899475{col 55}{space 4}-5.475718{col 68}{space 3} -3.55516
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.504964{col 27}{space 2} .4890035{col 55}{space 4}-2.463393{col 68}{space 3}-.5465344
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .7119137{col 27}{space 2}  .497294{col 55}{space 4}-.2627647{col 68}{space 3} 1.686592
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2}  16.3893{col 27}{space 2} 1.669843{col 55}{space 4} 13.42254{col 68}{space 3}  20.0118
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit imp_ gender_binary point1 point2  point3 point4  point5 point4_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-56611.798}  
Iteration 2:{space 3}log likelihood = {res:-56585.193}  
Iteration 3:{space 3}log likelihood = {res:-56585.157}  
Iteration 4:{space 3}log likelihood = {res:-56585.157}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-40166.628}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-40166.628}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34135.224}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33585.005}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33416.417}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33347.716}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33337.456}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33337.068}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33337.107}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33337.109}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   106.32
{txt}Log pseudolikelihood  = {res}-33337.109{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2} .1034449{col 27}{space 2} .4473876{col 38}{space 1}    0.23{col 47}{space 3}0.817{col 55}{space 4}-.7734188{col 68}{space 3} .9803085
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-4.177738{col 27}{space 2} .5865467{col 38}{space 1}   -7.12{col 47}{space 3}0.000{col 55}{space 4}-5.327348{col 68}{space 3}-3.028127
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-4.381063{col 27}{space 2} .6615231{col 38}{space 1}   -6.62{col 47}{space 3}0.000{col 55}{space 4}-5.677625{col 68}{space 3}-3.084502
{txt}{space 7}point3 {c |}{col 15}{res}{space 2} -.337046{col 27}{space 2}   .63825{col 38}{space 1}   -0.53{col 47}{space 3}0.597{col 55}{space 4}-1.587993{col 68}{space 3} .9139009
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-2.369842{col 27}{space 2} .6194979{col 38}{space 1}   -3.83{col 47}{space 3}0.000{col 55}{space 4}-3.584036{col 68}{space 3}-1.155648
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-3.137943{col 27}{space 2} .6314596{col 38}{space 1}   -4.97{col 47}{space 3}0.000{col 55}{space 4}-4.375581{col 68}{space 3}-1.900305
{txt}point4_Gender {c |}{col 15}{res}{space 2}-.7525914{col 27}{space 2} .7291858{col 38}{space 1}   -1.03{col 47}{space 3}0.302{col 55}{space 4}-2.181769{col 68}{space 3} .6765864
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -4.53468{col 27}{space 2} .4908898{col 55}{space 4}-5.496806{col 68}{space 3}-3.572553
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.524192{col 27}{space 2} .4898422{col 55}{space 4}-2.484265{col 68}{space 3} -.564119
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .6925735{col 27}{space 2} .4981008{col 55}{space 4}-.2836861{col 68}{space 3} 1.668833
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 16.50835{col 27}{space 2} 1.661372{col 55}{space 4} 13.55315{col 68}{space 3} 20.10792
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit imp_ gender_binary point1 point2  point3 point4  point5 point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59849.988}  
Iteration 1:{space 3}log likelihood = {res:-56564.298}  
Iteration 2:{space 3}log likelihood = {res:-56536.624}  
Iteration 3:{space 3}log likelihood = {res:-56536.584}  
Iteration 4:{space 3}log likelihood = {res:-56536.584}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-40149.935}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-40149.935}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34129.312}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33582.645}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33414.901}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33346.717}  
Iteration 5:{space 3}log pseudolikelihood = {res: -33336.57}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33336.194}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33336.232}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33336.234}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   107.87
{txt}Log pseudolikelihood  = {res}-33336.234{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 79:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.2214535{col 27}{space 2} .4105678{col 38}{space 1}   -0.54{col 47}{space 3}0.590{col 55}{space 4}-1.026152{col 68}{space 3} .5832445
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-4.077886{col 27}{space 2} .5840494{col 38}{space 1}   -6.98{col 47}{space 3}0.000{col 55}{space 4}-5.222601{col 68}{space 3} -2.93317
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-4.295114{col 27}{space 2} .6609017{col 38}{space 1}   -6.50{col 47}{space 3}0.000{col 55}{space 4}-5.590457{col 68}{space 3} -2.99977
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.3036217{col 27}{space 2} .6366803{col 38}{space 1}   -0.48{col 47}{space 3}0.633{col 55}{space 4}-1.551492{col 68}{space 3} .9442488
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-2.516711{col 27}{space 2} .5839315{col 38}{space 1}   -4.31{col 47}{space 3}0.000{col 55}{space 4}-3.661196{col 68}{space 3}-1.372226
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-3.372642{col 27}{space 2} .6437603{col 38}{space 1}   -5.24{col 47}{space 3}0.000{col 55}{space 4}-4.634389{col 68}{space 3}-2.110895
{txt}point5_Gender {c |}{col 15}{res}{space 2} 2.049688{col 27}{space 2} 1.260214{col 38}{space 1}    1.63{col 47}{space 3}0.104{col 55}{space 4}-.4202867{col 68}{space 3} 4.519662
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.557905{col 27}{space 2}  .490047{col 55}{space 4}-5.518379{col 68}{space 3}-3.597431
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -1.54745{col 27}{space 2} .4890231{col 55}{space 4}-2.505918{col 68}{space 3}-.5889823
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .6694222{col 27}{space 2} .4975116{col 55}{space 4}-.3056827{col 68}{space 3} 1.644527
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
    /sigma2_u {c |}{col 15}{res}{space 2} 16.44408{col 27}{space 2} 1.660433{col 55}{space 4} 13.49149{col 68}{space 3} 20.04284
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. xtologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point1_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res:-56044.721}  
Iteration 2:{space 3}log likelihood = {res:-56012.565}  
Iteration 3:{space 3}log likelihood = {res:-56012.514}  
Iteration 4:{space 3}log likelihood = {res:-56012.514}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-39885.034}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-39885.034}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34122.569}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33606.958}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33443.539}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33376.633}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33366.876}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33366.523}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33366.567}  
Iteration 8:{space 3}log pseudolikelihood = {res: -33366.57}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   119.36
{txt}Log pseudolikelihood  = {res} -33366.57{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-1.448306{col 31}{space 2} .4898391{col 42}{space 1}   -2.96{col 51}{space 3}0.003{col 59}{space 4}-2.408373{col 72}{space 3} -.488239
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-4.104705{col 31}{space 2} .6105531{col 42}{space 1}   -6.72{col 51}{space 3}0.000{col 59}{space 4}-5.301367{col 72}{space 3}-2.908043
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-4.330861{col 31}{space 2} .6485541{col 42}{space 1}   -6.68{col 51}{space 3}0.000{col 59}{space 4}-5.602003{col 72}{space 3}-3.059718
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.5413657{col 31}{space 2}  .633676{col 42}{space 1}   -0.85{col 51}{space 3}0.393{col 59}{space 4}-1.783348{col 72}{space 3} .7006163
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-2.515466{col 31}{space 2} .5940301{col 42}{space 1}   -4.23{col 51}{space 3}0.000{col 59}{space 4}-3.679743{col 72}{space 3}-1.351188
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-3.053641{col 31}{space 2} .6262984{col 42}{space 1}   -4.88{col 51}{space 3}0.000{col 59}{space 4}-4.281163{col 72}{space 3}-1.826118
{txt}{space 4}point1_Gender {c |}{col 19}{res}{space 2} .5108848{col 31}{space 2} .7777529{col 42}{space 1}    0.66{col 51}{space 3}0.511{col 59}{space 4}-1.013483{col 72}{space 3} 2.035252
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-4.521516{col 31}{space 2} .4846492{col 59}{space 4}-5.471411{col 72}{space 3}-3.571621
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2} -1.58615{col 31}{space 2}   .48416{col 59}{space 4}-2.535086{col 72}{space 3}-.6372138
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .6148445{col 31}{space 2} .4925653{col 59}{space 4}-.3505657{col 72}{space 3} 1.580255
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 16.43932{col 31}{space 2} 1.672848{col 59}{space 4} 13.46686{col 72}{space 3} 20.06787
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point2_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res: -55886.45}  
Iteration 2:{space 3}log likelihood = {res: -55852.05}  
Iteration 3:{space 3}log likelihood = {res: -55851.99}  
Iteration 4:{space 3}log likelihood = {res: -55851.99}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-39809.542}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-39809.542}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34113.736}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33603.785}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33441.696}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33375.513}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33365.951}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33365.613}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33365.656}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33365.659}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   121.25
{txt}Log pseudolikelihood  = {res}-33365.659{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-1.632095{col 31}{space 2} .4379255{col 42}{space 1}   -3.73{col 51}{space 3}0.000{col 59}{space 4}-2.490413{col 72}{space 3}-.7737771
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-3.857286{col 31}{space 2} .5784249{col 42}{space 1}   -6.67{col 51}{space 3}0.000{col 59}{space 4}-4.990978{col 72}{space 3}-2.723594
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-4.760319{col 31}{space 2} .6996039{col 42}{space 1}   -6.80{col 51}{space 3}0.000{col 59}{space 4}-6.131517{col 72}{space 3} -3.38912
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.5224412{col 31}{space 2} .6320442{col 42}{space 1}   -0.83{col 51}{space 3}0.408{col 59}{space 4}-1.761225{col 72}{space 3} .7163426
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-2.475476{col 31}{space 2} .5911272{col 42}{space 1}   -4.19{col 51}{space 3}0.000{col 59}{space 4}-3.634064{col 72}{space 3}-1.316888
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-3.042501{col 31}{space 2} .6263096{col 42}{space 1}   -4.86{col 51}{space 3}0.000{col 59}{space 4}-4.270046{col 72}{space 3}-1.814957
{txt}{space 4}point2_Gender {c |}{col 19}{res}{space 2} 1.471004{col 31}{space 2} .9876706{col 42}{space 1}    1.49{col 51}{space 3}0.136{col 59}{space 4}-.4647949{col 72}{space 3} 3.406803
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-4.534665{col 31}{space 2} .4840953{col 59}{space 4}-5.483474{col 72}{space 3}-3.585855
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.599187{col 31}{space 2} .4836895{col 59}{space 4}-2.547201{col 72}{space 3}-.6511733
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .6017758{col 31}{space 2} .4917478{col 59}{space 4}-.3620322{col 72}{space 3} 1.565584
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 16.35436{col 31}{space 2} 1.672799{col 59}{space 4} 13.38347{col 72}{space 3} 19.98473
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point3_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res:-55676.132}  
Iteration 2:{space 3}log likelihood = {res:-55635.322}  
Iteration 3:{space 3}log likelihood = {res:-55635.238}  
Iteration 4:{space 3}log likelihood = {res:-55635.238}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-39687.904}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-39687.904}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34084.241}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33593.655}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33435.215}  
Iteration 4:{space 3}log pseudolikelihood = {res: -33371.13}  
Iteration 5:{space 3}log pseudolikelihood = {res: -33362.11}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33361.817}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33361.854}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33361.857}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   126.37
{txt}Log pseudolikelihood  = {res}-33361.857{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.6275858{col 31}{space 2} .4142866{col 42}{space 1}   -1.51{col 51}{space 3}0.130{col 59}{space 4}-1.439573{col 72}{space 3} .1844011
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-4.153054{col 31}{space 2} .5742025{col 42}{space 1}   -7.23{col 51}{space 3}0.000{col 59}{space 4} -5.27847{col 72}{space 3}-3.027638
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-4.526306{col 31}{space 2} .6398332{col 42}{space 1}   -7.07{col 51}{space 3}0.000{col 59}{space 4}-5.780356{col 72}{space 3}-3.272256
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.0646084{col 31}{space 2}  .657373{col 42}{space 1}   -0.10{col 51}{space 3}0.922{col 59}{space 4}-1.353036{col 72}{space 3} 1.223819
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-2.686677{col 31}{space 2} .5910208{col 42}{space 1}   -4.55{col 51}{space 3}0.000{col 59}{space 4}-3.845056{col 72}{space 3}-1.528297
{txt}{space 11}point5 {c |}{col 19}{res}{space 2} -3.08853{col 31}{space 2} .6197193{col 42}{space 1}   -4.98{col 51}{space 3}0.000{col 59}{space 4}-4.303158{col 72}{space 3}-1.873903
{txt}{space 4}point3_Gender {c |}{col 19}{res}{space 2} -3.23825{col 31}{space 2} 1.003608{col 42}{space 1}   -3.23{col 51}{space 3}0.001{col 59}{space 4}-5.205285{col 72}{space 3}-1.271215
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-4.462708{col 31}{space 2} .4817401{col 59}{space 4}-5.406901{col 72}{space 3}-3.518515
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.527337{col 31}{space 2} .4814588{col 59}{space 4}-2.470979{col 72}{space 3}-.5836954
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .6739779{col 31}{space 2} .4898856{col 59}{space 4}-.2861801{col 72}{space 3} 1.634136
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2}  16.0723{col 31}{space 2} 1.663065{col 59}{space 4} 13.12202{col 72}{space 3}  19.6859
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point4_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res:-56028.584}  
Iteration 2:{space 3}log likelihood = {res:-55996.347}  
Iteration 3:{space 3}log likelihood = {res:-55996.295}  
Iteration 4:{space 3}log likelihood = {res:-55996.295}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-39879.295}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-39879.295}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34122.948}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33606.818}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33443.485}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33376.625}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33366.877}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33366.525}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33366.568}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33366.571}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   121.40
{txt}Log pseudolikelihood  = {res}-33366.571{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-1.225928{col 31}{space 2} .4421197{col 42}{space 1}   -2.77{col 51}{space 3}0.006{col 59}{space 4}-2.092467{col 72}{space 3}-.3593895
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-3.981264{col 31}{space 2} .5789448{col 42}{space 1}   -6.88{col 51}{space 3}0.000{col 59}{space 4}-5.115975{col 72}{space 3}-2.846553
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-4.387143{col 31}{space 2} .6456966{col 42}{space 1}   -6.79{col 51}{space 3}0.000{col 59}{space 4}-5.652685{col 72}{space 3}-3.121601
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.5638467{col 31}{space 2} .6334732{col 42}{space 1}   -0.89{col 51}{space 3}0.373{col 59}{space 4}-1.805431{col 72}{space 3} .6777379
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-2.390753{col 31}{space 2} .6122112{col 42}{space 1}   -3.91{col 51}{space 3}0.000{col 59}{space 4}-3.590665{col 72}{space 3}-1.190842
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-3.064992{col 31}{space 2} .6252035{col 42}{space 1}   -4.90{col 51}{space 3}0.000{col 59}{space 4}-4.290369{col 72}{space 3}-1.839616
{txt}{space 4}point4_Gender {c |}{col 19}{res}{space 2}-.6048431{col 31}{space 2} .9013475{col 42}{space 1}   -0.67{col 51}{space 3}0.502{col 59}{space 4}-2.371452{col 72}{space 3} 1.161766
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-4.505627{col 31}{space 2}  .484297{col 59}{space 4}-5.454831{col 72}{space 3}-3.556422
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.570218{col 31}{space 2} .4840064{col 59}{space 4}-2.518853{col 72}{space 3}-.6215829
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}  .630751{col 31}{space 2} .4923447{col 59}{space 4}-.3342268{col 72}{space 3} 1.595729
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 16.43536{col 31}{space 2} 1.673491{col 59}{space 4} 13.46194{col 72}{space 3} 20.06553
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. xtologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point5_Gender, vce(cluster disposición)
{res}{txt}
Fitting comparison model:

Iteration 0:{space 3}log likelihood = {res:-59560.482}  
Iteration 1:{space 3}log likelihood = {res: -55937.03}  
Iteration 2:{space 3}log likelihood = {res:-55903.017}  
Iteration 3:{space 3}log likelihood = {res:-55902.959}  
Iteration 4:{space 3}log likelihood = {res:-55902.959}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-39843.965}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-39843.965}  
Iteration 1:{space 3}log pseudolikelihood = {res:-34112.616}  
Iteration 2:{space 3}log pseudolikelihood = {res:-33602.136}  
Iteration 3:{space 3}log pseudolikelihood = {res:-33440.405}  
Iteration 4:{space 3}log pseudolikelihood = {res:-33374.526}  
Iteration 5:{space 3}log pseudolikelihood = {res:-33365.002}  
Iteration 6:{space 3}log pseudolikelihood = {res:-33364.671}  
Iteration 7:{space 3}log pseudolikelihood = {res:-33364.712}  
Iteration 8:{space 3}log pseudolikelihood = {res:-33364.715}  
{res}
{txt}Random-effects ordered logistic regression{col 49}Number of obs{col 67}={col 69}{res}    44,506
{txt}Group variable: {res}disposición{col 49}{txt}Number of groups{col 67}={col 69}{res}       578

{txt}Random effects u_i ~ {res}Gaussian{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}        77
{txt}{col 63}avg{col 67}={col 69}{res}      77.0
{txt}{col 63}max{col 67}={col 69}{res}        77

{txt}Integration method: {res}mvaghermite{txt}{col 49}Integration pts.{col 67}={col 70}{res}       12

{txt}{col 49}Wald chi2({res}7{txt}){col 67}={col 70}{res}   124.87
{txt}Log pseudolikelihood  = {res}-33364.715{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000

{txt}{ralign 83:(Std. Err. adjusted for {res:579} clusters in disposición)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-1.606163{col 31}{space 2} .4114919{col 42}{space 1}   -3.90{col 51}{space 3}0.000{col 59}{space 4}-2.412672{col 72}{space 3}-.7996533
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-3.863953{col 31}{space 2} .5775748{col 42}{space 1}   -6.69{col 51}{space 3}0.000{col 59}{space 4}-4.995979{col 72}{space 3}-2.731927
{txt}{space 11}point2 {c |}{col 19}{res}{space 2} -4.28608{col 31}{space 2} .6449859{col 42}{space 1}   -6.65{col 51}{space 3}0.000{col 59}{space 4}-5.550229{col 72}{space 3}-3.021931
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.5248769{col 31}{space 2} .6311705{col 42}{space 1}   -0.83{col 51}{space 3}0.406{col 59}{space 4}-1.761948{col 72}{space 3} .7121946
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-2.480515{col 31}{space 2} .5909011{col 42}{space 1}   -4.20{col 51}{space 3}0.000{col 59}{space 4} -3.63866{col 72}{space 3} -1.32237
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-3.386528{col 31}{space 2} .6364455{col 42}{space 1}   -5.32{col 51}{space 3}0.000{col 59}{space 4}-4.633938{col 72}{space 3}-2.139118
{txt}{space 4}point5_Gender {c |}{col 19}{res}{space 2} 2.790936{col 31}{space 2} 1.189261{col 42}{space 1}    2.35{col 51}{space 3}0.019{col 59}{space 4} .4600262{col 72}{space 3} 5.121845
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-4.532795{col 31}{space 2} .4830541{col 59}{space 4}-5.479563{col 72}{space 3}-3.586026
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.597388{col 31}{space 2} .4828383{col 59}{space 4}-2.543734{col 72}{space 3}-.6510423
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2} .6036977{col 31}{space 2} .4916358{col 59}{space 4}-.3598908{col 72}{space 3} 1.567286
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /sigma2_u {c |}{col 19}{res}{space 2} 16.30441{col 31}{space 2}  1.66547{col 59}{space 4} 13.34616{col 72}{space 3} 19.91838
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. //Appendix Table 6d. last month of the data with interaction
. 
. ologit imp_ gender_binary point1 point2  point3 point4  point5 point1_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.83151}  
Iteration 2:{space 3}log pseudolikelihood = {res:-711.33047}  
Iteration 3:{space 3}log pseudolikelihood = {res:-711.32941}  
Iteration 4:{space 3}log pseudolikelihood = {res:-711.32941}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     91.45
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-711.32941{txt}{col 49}Pseudo R2{col 67}= {res}    0.0649

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.1248109{col 27}{space 2} .2437207{col 38}{space 1}   -0.51{col 47}{space 3}0.609{col 55}{space 4}-.6024947{col 68}{space 3} .3528729
{txt}{space 7}point1 {c |}{col 15}{res}{space 2} -2.03533{col 27}{space 2} .3018783{col 38}{space 1}   -6.74{col 47}{space 3}0.000{col 55}{space 4}-2.627001{col 68}{space 3} -1.44366
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-2.052468{col 27}{space 2} .3381427{col 38}{space 1}   -6.07{col 47}{space 3}0.000{col 55}{space 4}-2.715216{col 68}{space 3}-1.389721
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.3293625{col 27}{space 2} .3058943{col 38}{space 1}   -1.08{col 47}{space 3}0.282{col 55}{space 4}-.9289043{col 68}{space 3} .2701793
{txt}{space 7}point4 {c |}{col 15}{res}{space 2} -1.06961{col 27}{space 2} .3133427{col 38}{space 1}   -3.41{col 47}{space 3}0.001{col 55}{space 4} -1.68375{col 68}{space 3}-.4554694
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-1.057344{col 27}{space 2} .3225078{col 38}{space 1}   -3.28{col 47}{space 3}0.001{col 55}{space 4}-1.689448{col 68}{space 3}-.4252401
{txt}point1_Gender {c |}{col 15}{res}{space 2} .0960848{col 27}{space 2} .3454648{col 38}{space 1}    0.28{col 47}{space 3}0.781{col 55}{space 4}-.5810138{col 68}{space 3} .7731834
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.267426{col 27}{space 2} .2969055{col 55}{space 4} -3.84935{col 68}{space 3}-2.685502
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -1.19049{col 27}{space 2} .2560938{col 55}{space 4}-1.692425{col 68}{space 3}-.6885555
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.1993213{col 27}{space 2}  .243772{col 55}{space 4}-.6771057{col 68}{space 3} .2784632
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit imp_ gender_binary point1 point2  point3 point4  point5 point2_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.23748}  
Iteration 2:{space 3}log pseudolikelihood = {res:-709.69171}  
Iteration 3:{space 3}log pseudolikelihood = {res:-709.69043}  
Iteration 4:{space 3}log pseudolikelihood = {res:-709.69043}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     93.55
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-709.69043{txt}{col 49}Pseudo R2{col 67}= {res}    0.0670

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.2737456{col 27}{space 2} .2011413{col 38}{space 1}   -1.36{col 47}{space 3}0.174{col 55}{space 4}-.6679753{col 68}{space 3} .1204841
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-1.959579{col 27}{space 2}  .293892{col 38}{space 1}   -6.67{col 47}{space 3}0.000{col 55}{space 4}-2.535597{col 68}{space 3}-1.383562
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-2.295543{col 27}{space 2} .3628336{col 38}{space 1}   -6.33{col 47}{space 3}0.000{col 55}{space 4}-3.006684{col 68}{space 3}-1.584403
{txt}{space 7}point3 {c |}{col 15}{res}{space 2} -.311733{col 27}{space 2} .3059924{col 38}{space 1}   -1.02{col 47}{space 3}0.308{col 55}{space 4}-.9114671{col 68}{space 3} .2880011
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-1.037541{col 27}{space 2} .3119584{col 38}{space 1}   -3.33{col 47}{space 3}0.001{col 55}{space 4}-1.648969{col 68}{space 3}-.4261141
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-1.054178{col 27}{space 2} .3240506{col 38}{space 1}   -3.25{col 47}{space 3}0.001{col 55}{space 4}-1.689305{col 68}{space 3}-.4190503
{txt}point2_Gender {c |}{col 15}{res}{space 2} .8527465{col 27}{space 2} .4956838{col 38}{space 1}    1.72{col 47}{space 3}0.085{col 55}{space 4}-.1187758{col 68}{space 3} 1.824269
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.291086{col 27}{space 2} .2986516{col 55}{space 4}-3.876433{col 68}{space 3} -2.70574
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.202316{col 27}{space 2} .2569951{col 55}{space 4}-1.706018{col 68}{space 3}-.6986154
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.2067434{col 27}{space 2} .2446605{col 55}{space 4} -.686269{col 68}{space 3} .2727823
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit imp_ gender_binary point1 point2  point3 point4  point5 point3_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-709.30284}  
Iteration 2:{space 3}log pseudolikelihood = {res:-708.76207}  
Iteration 3:{space 3}log pseudolikelihood = {res:-708.76084}  
Iteration 4:{space 3}log pseudolikelihood = {res:-708.76084}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     93.39
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-708.76084{txt}{col 49}Pseudo R2{col 67}= {res}    0.0682

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2} .1232593{col 27}{space 2} .2022211{col 38}{space 1}    0.61{col 47}{space 3}0.542{col 55}{space 4}-.2730868{col 68}{space 3} .5196053
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-2.086868{col 27}{space 2} .2960772{col 38}{space 1}   -7.05{col 47}{space 3}0.000{col 55}{space 4}-2.667169{col 68}{space 3}-1.506568
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-2.125227{col 27}{space 2}  .338232{col 38}{space 1}   -6.28{col 47}{space 3}0.000{col 55}{space 4} -2.78815{col 68}{space 3}-1.462305
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.1616871{col 27}{space 2} .3175252{col 38}{space 1}   -0.51{col 47}{space 3}0.611{col 55}{space 4}-.7840252{col 68}{space 3} .4606509
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-1.132952{col 27}{space 2} .3144397{col 38}{space 1}   -3.60{col 47}{space 3}0.000{col 55}{space 4}-1.749243{col 68}{space 3}-.5166619
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-1.072116{col 27}{space 2} .3230519{col 38}{space 1}   -3.32{col 47}{space 3}0.001{col 55}{space 4}-1.705286{col 68}{space 3}-.4389462
{txt}point3_Gender {c |}{col 15}{res}{space 2}-1.061647{col 27}{space 2} .4686556{col 38}{space 1}   -2.27{col 47}{space 3}0.023{col 55}{space 4}-1.980195{col 68}{space 3}-.1430988
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.265747{col 27}{space 2} .2971563{col 55}{space 4}-3.848163{col 68}{space 3}-2.683331
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.183218{col 27}{space 2} .2564789{col 55}{space 4}-1.685908{col 68}{space 3}-.6805289
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.1814673{col 27}{space 2} .2437915{col 55}{space 4}-.6592898{col 68}{space 3} .2963553
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit imp_ gender_binary point1 point2  point3 point4  point5 point4_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.50887}  
Iteration 2:{space 3}log pseudolikelihood = {res:-710.99819}  
Iteration 3:{space 3}log pseudolikelihood = {res:-710.99708}  
Iteration 4:{space 3}log pseudolikelihood = {res:-710.99708}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     91.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-710.99708{txt}{col 49}Pseudo R2{col 67}= {res}    0.0653

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.0244132{col 27}{space 2} .2115646{col 38}{space 1}   -0.12{col 47}{space 3}0.908{col 55}{space 4}-.4390722{col 68}{space 3} .3902459
{txt}{space 7}point1 {c |}{col 15}{res}{space 2} -2.03247{col 27}{space 2} .2947532{col 38}{space 1}   -6.90{col 47}{space 3}0.000{col 55}{space 4}-2.610176{col 68}{space 3}-1.454765
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-2.079824{col 27}{space 2}  .337579{col 38}{space 1}   -6.16{col 47}{space 3}0.000{col 55}{space 4}-2.741466{col 68}{space 3}-1.418181
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.3415608{col 27}{space 2} .3056806{col 38}{space 1}   -1.12{col 47}{space 3}0.264{col 55}{space 4}-.9406837{col 68}{space 3} .2575621
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-.9661957{col 27}{space 2} .3297526{col 38}{space 1}   -2.93{col 47}{space 3}0.003{col 55}{space 4}-1.612499{col 68}{space 3}-.3198925
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-1.062147{col 27}{space 2} .3223786{col 38}{space 1}   -3.29{col 47}{space 3}0.001{col 55}{space 4}-1.693997{col 68}{space 3}-.4302963
{txt}point4_Gender {c |}{col 15}{res}{space 2}-.4314544{col 27}{space 2} .4116192{col 38}{space 1}   -1.05{col 47}{space 3}0.295{col 55}{space 4}-1.238213{col 68}{space 3} .3753044
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.264299{col 27}{space 2} .2964964{col 55}{space 4}-3.845422{col 68}{space 3}-2.683177
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -1.18526{col 27}{space 2} .2558404{col 55}{space 4}-1.686698{col 68}{space 3}-.6838219
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} -.192869{col 27}{space 2} .2435764{col 55}{space 4}  -.67027{col 68}{space 3} .2845321
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit imp_ gender_binary point1 point2  point3 point4  point5 point5_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-760.66636}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.10766}  
Iteration 2:{space 3}log pseudolikelihood = {res:-710.58873}  
Iteration 3:{space 3}log pseudolikelihood = {res:-710.58758}  
Iteration 4:{space 3}log pseudolikelihood = {res:-710.58758}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     91.68
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-710.58758{txt}{col 49}Pseudo R2{col 67}= {res}    0.0658

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}         imp_{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gender_binary {c |}{col 15}{res}{space 2}-.1690842{col 27}{space 2} .1882102{col 38}{space 1}   -0.90{col 47}{space 3}0.369{col 55}{space 4}-.5379694{col 68}{space 3} .1998009
{txt}{space 7}point1 {c |}{col 15}{res}{space 2}-1.987061{col 27}{space 2} .2925586{col 38}{space 1}   -6.79{col 47}{space 3}0.000{col 55}{space 4}-2.560465{col 68}{space 3}-1.413657
{txt}{space 7}point2 {c |}{col 15}{res}{space 2}-2.042901{col 27}{space 2}  .337188{col 38}{space 1}   -6.06{col 47}{space 3}0.000{col 55}{space 4}-2.703777{col 68}{space 3}-1.382025
{txt}{space 7}point3 {c |}{col 15}{res}{space 2}-.3242116{col 27}{space 2} .3053234{col 38}{space 1}   -1.06{col 47}{space 3}0.288{col 55}{space 4}-.9226345{col 68}{space 3} .2742113
{txt}{space 7}point4 {c |}{col 15}{res}{space 2}-1.060276{col 27}{space 2} .3110416{col 38}{space 1}   -3.41{col 47}{space 3}0.001{col 55}{space 4}-1.669907{col 68}{space 3}-.4506457
{txt}{space 7}point5 {c |}{col 15}{res}{space 2}-1.141404{col 27}{space 2} .3259784{col 38}{space 1}   -3.50{col 47}{space 3}0.000{col 55}{space 4} -1.78031{col 68}{space 3}-.5024979
{txt}point5_Gender {c |}{col 15}{res}{space 2} .8373638{col 27}{space 2}  .843688{col 38}{space 1}    0.99{col 47}{space 3}0.321{col 55}{space 4}-.8162342{col 68}{space 3} 2.490962
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-3.272462{col 27}{space 2} .2968272{col 55}{space 4}-3.854232{col 68}{space 3}-2.690691
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.194986{col 27}{space 2} .2562472{col 55}{space 4}-1.697221{col 68}{space 3}-.6927507
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-.2012842{col 27}{space 2} .2440437{col 55}{space 4} -.679601{col 68}{space 3} .2770326
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. ologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point1_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.69244}  
Iteration 2:{space 3}log pseudolikelihood = {res:-709.99706}  
Iteration 3:{space 3}log pseudolikelihood = {res:-709.99507}  
Iteration 4:{space 3}log pseudolikelihood = {res:-709.99507}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     99.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-709.99507{txt}{col 49}Pseudo R2{col 67}= {res}    0.0729

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.6506554{col 31}{space 2} .2367304{col 42}{space 1}   -2.75{col 51}{space 3}0.006{col 59}{space 4}-1.114639{col 72}{space 3}-.1866723
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-2.087155{col 31}{space 2} .3060677{col 42}{space 1}   -6.82{col 51}{space 3}0.000{col 59}{space 4}-2.687037{col 72}{space 3}-1.487274
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-2.108131{col 31}{space 2} .3389421{col 42}{space 1}   -6.22{col 51}{space 3}0.000{col 59}{space 4}-2.772446{col 72}{space 3}-1.443817
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.4370581{col 31}{space 2} .3092709{col 42}{space 1}   -1.41{col 51}{space 3}0.158{col 59}{space 4}-1.043218{col 72}{space 3} .1691018
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-1.059562{col 31}{space 2} .3205438{col 42}{space 1}   -3.31{col 51}{space 3}0.001{col 59}{space 4}-1.687816{col 72}{space 3}-.4313076
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-1.062896{col 31}{space 2} .3265415{col 42}{space 1}   -3.26{col 51}{space 3}0.001{col 59}{space 4}-1.702906{col 72}{space 3}-.4228868
{txt}{space 4}point1_Gender {c |}{col 19}{res}{space 2} .4230072{col 31}{space 2} .3372228{col 42}{space 1}    1.25{col 51}{space 3}0.210{col 59}{space 4}-.2379373{col 72}{space 3} 1.083952
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-3.320781{col 31}{space 2} .3005613{col 59}{space 4} -3.90987{col 72}{space 3}-2.731691
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.245419{col 31}{space 2}   .26107{col 59}{space 4}-1.757107{col 72}{space 3}-.7337312
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-.2235442{col 31}{space 2} .2481209{col 59}{space 4}-.7098522{col 72}{space 3} .2627637
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point2_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-710.12781}  
Iteration 2:{space 3}log pseudolikelihood = {res:-709.41548}  
Iteration 3:{space 3}log pseudolikelihood = {res:-709.41337}  
Iteration 4:{space 3}log pseudolikelihood = {res:-709.41337}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    100.80
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-709.41337{txt}{col 49}Pseudo R2{col 67}= {res}    0.0736

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.6748299{col 31}{space 2} .2043958{col 42}{space 1}   -3.30{col 51}{space 3}0.001{col 59}{space 4}-1.075438{col 72}{space 3}-.2742215
{txt}{space 11}point1 {c |}{col 19}{res}{space 2} -1.92434{col 31}{space 2} .2986942{col 42}{space 1}   -6.44{col 51}{space 3}0.000{col 59}{space 4} -2.50977{col 72}{space 3} -1.33891
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-2.333982{col 31}{space 2} .3656535{col 42}{space 1}   -6.38{col 51}{space 3}0.000{col 59}{space 4} -3.05065{col 72}{space 3}-1.617314
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.4342876{col 31}{space 2} .3090037{col 42}{space 1}   -1.41{col 51}{space 3}0.160{col 59}{space 4}-1.039924{col 72}{space 3} .1713485
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-1.055288{col 31}{space 2} .3197624{col 42}{space 1}   -3.30{col 51}{space 3}0.001{col 59}{space 4}-1.682011{col 72}{space 3}-.4285655
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-1.062733{col 31}{space 2} .3269741{col 42}{space 1}   -3.25{col 51}{space 3}0.001{col 59}{space 4} -1.70359{col 72}{space 3}-.4218755
{txt}{space 4}point2_Gender {c |}{col 19}{res}{space 2} .6801837{col 31}{space 2} .4468606{col 42}{space 1}    1.52{col 51}{space 3}0.128{col 59}{space 4} -.195647{col 72}{space 3} 1.556014
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-3.328132{col 31}{space 2} .3009277{col 59}{space 4} -3.91794{col 72}{space 3}-2.738325
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.247268{col 31}{space 2} .2613808{col 59}{space 4}-1.759565{col 72}{space 3}-.7349714
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-.2246353{col 31}{space 2} .2483428{col 59}{space 4}-.7113783{col 72}{space 3} .2621078
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point3_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-704.98061}  
Iteration 2:{space 3}log pseudolikelihood = {res:-704.14827}  
Iteration 3:{space 3}log pseudolikelihood = {res:-704.14549}  
Iteration 4:{space 3}log pseudolikelihood = {res:-704.14549}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    104.44
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-704.14549{txt}{col 49}Pseudo R2{col 67}= {res}    0.0805

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.1847458{col 31}{space 2} .1962524{col 42}{space 1}   -0.94{col 51}{space 3}0.347{col 59}{space 4}-.5693933{col 72}{space 3} .1999018
{txt}{space 11}point1 {c |}{col 19}{res}{space 2} -2.09558{col 31}{space 2} .3007405{col 42}{space 1}   -6.97{col 51}{space 3}0.000{col 59}{space 4}-2.685021{col 72}{space 3} -1.50614
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-2.257334{col 31}{space 2} .3395679{col 42}{space 1}   -6.65{col 51}{space 3}0.000{col 59}{space 4}-2.922874{col 72}{space 3}-1.591793
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.1810024{col 31}{space 2} .3215891{col 42}{space 1}   -0.56{col 51}{space 3}0.574{col 59}{space 4}-.8113054{col 72}{space 3} .4493007
{txt}{space 11}point4 {c |}{col 19}{res}{space 2} -1.17286{col 31}{space 2} .3215645{col 42}{space 1}   -3.65{col 51}{space 3}0.000{col 59}{space 4}-1.803115{col 72}{space 3}-.5426055
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}   -1.094{col 31}{space 2}  .326594{col 42}{space 1}   -3.35{col 51}{space 3}0.001{col 59}{space 4}-1.734113{col 72}{space 3}-.4538878
{txt}{space 4}point3_Gender {c |}{col 19}{res}{space 2}-1.654593{col 31}{space 2} .4567021{col 42}{space 1}   -3.62{col 51}{space 3}0.000{col 59}{space 4}-2.549713{col 72}{space 3}-.7594736
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-3.322087{col 31}{space 2}  .301134{col 59}{space 4}-3.912299{col 72}{space 3}-2.731875
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.233649{col 31}{space 2}  .261493{col 59}{space 4}-1.746166{col 72}{space 3} -.721132
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-.1872825{col 31}{space 2} .2471376{col 59}{space 4}-.6716633{col 72}{space 3} .2970982
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point4_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.00434}  
Iteration 2:{space 3}log pseudolikelihood = {res:-710.31084}  
Iteration 3:{space 3}log pseudolikelihood = {res:-710.30883}  
Iteration 4:{space 3}log pseudolikelihood = {res:-710.30883}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    100.80
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-710.30883{txt}{col 49}Pseudo R2{col 67}= {res}    0.0725

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.4756999{col 31}{space 2} .2008006{col 42}{space 1}   -2.37{col 51}{space 3}0.018{col 59}{space 4}-.8692619{col 72}{space 3}-.0821379
{txt}{space 11}point1 {c |}{col 19}{res}{space 2}-1.982266{col 31}{space 2} .2972556{col 42}{space 1}   -6.67{col 51}{space 3}0.000{col 59}{space 4}-2.564876{col 72}{space 3}-1.399656
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-2.154701{col 31}{space 2} .3361265{col 42}{space 1}   -6.41{col 51}{space 3}0.000{col 59}{space 4}-2.813497{col 72}{space 3}-1.495906
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.4587871{col 31}{space 2} .3081482{col 42}{space 1}   -1.49{col 51}{space 3}0.137{col 59}{space 4}-1.062746{col 72}{space 3} .1451723
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-1.003287{col 31}{space 2} .3338022{col 42}{space 1}   -3.01{col 51}{space 3}0.003{col 59}{space 4}-1.657527{col 72}{space 3}-.3490464
{txt}{space 11}point5 {c |}{col 19}{res}{space 2} -1.06979{col 31}{space 2} .3250465{col 42}{space 1}   -3.29{col 51}{space 3}0.001{col 59}{space 4}-1.706869{col 72}{space 3}-.4327101
{txt}{space 4}point4_Gender {c |}{col 19}{res}{space 2}-.3347748{col 31}{space 2} .4694576{col 42}{space 1}   -0.71{col 51}{space 3}0.476{col 59}{space 4}-1.254895{col 72}{space 3} .5853452
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2}-3.309317{col 31}{space 2} .2995469{col 59}{space 4}-3.896418{col 72}{space 3}-2.722216
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.232757{col 31}{space 2} .2599341{col 59}{space 4}-1.742218{col 72}{space 3}-.7232952
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-.2131274{col 31}{space 2} .2469127{col 59}{space 4}-.6970674{col 72}{space 3} .2708126
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit gender_imp_recode  gender_binary point1 point2  point3 point4  point5 point5_Gender if smdate =="apr_2023", vce(robust)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-765.79169}  
Iteration 1:{space 3}log pseudolikelihood = {res: -709.9066}  
Iteration 2:{space 3}log pseudolikelihood = {res:-709.18254}  
Iteration 3:{space 3}log pseudolikelihood = {res:-709.18033}  
Iteration 4:{space 3}log pseudolikelihood = {res:-709.18033}  
{res}
{txt}Ordered logistic regression{col 49}Number of obs{col 67}= {res}       578
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    101.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-709.18033{txt}{col 49}Pseudo R2{col 67}= {res}    0.0739

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}gender_imp_recode{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gender_binary {c |}{col 19}{res}{space 2}-.6259687{col 31}{space 2} .1853796{col 42}{space 1}   -3.38{col 51}{space 3}0.001{col 59}{space 4} -.989306{col 72}{space 3}-.2626315
{txt}{space 11}point1 {c |}{col 19}{res}{space 2} -1.93936{col 31}{space 2} .2975273{col 42}{space 1}   -6.52{col 51}{space 3}0.000{col 59}{space 4}-2.522502{col 72}{space 3}-1.356217
{txt}{space 11}point2 {c |}{col 19}{res}{space 2}-2.117715{col 31}{space 2} .3369082{col 42}{space 1}   -6.29{col 51}{space 3}0.000{col 59}{space 4}-2.778043{col 72}{space 3}-1.457387
{txt}{space 11}point3 {c |}{col 19}{res}{space 2}-.4405027{col 31}{space 2} .3085165{col 42}{space 1}   -1.43{col 51}{space 3}0.153{col 59}{space 4}-1.045184{col 72}{space 3} .1641786
{txt}{space 11}point4 {c |}{col 19}{res}{space 2}-1.065882{col 31}{space 2} .3190496{col 42}{space 1}   -3.34{col 51}{space 3}0.001{col 59}{space 4}-1.691208{col 72}{space 3}-.4405566
{txt}{space 11}point5 {c |}{col 19}{res}{space 2}-1.180817{col 31}{space 2} .3305046{col 42}{space 1}   -3.57{col 51}{space 3}0.000{col 59}{space 4}-1.828594{col 72}{space 3}-.5330403
{txt}{space 4}point5_Gender {c |}{col 19}{res}{space 2} 1.064704{col 31}{space 2} .7486512{col 42}{space 1}    1.42{col 51}{space 3}0.155{col 59}{space 4}-.4026258{col 72}{space 3} 2.532033
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/cut1 {c |}{col 19}{res}{space 2} -3.32477{col 31}{space 2} .3005148{col 59}{space 4}-3.913768{col 72}{space 3}-2.735771
{txt}{space 12}/cut2 {c |}{col 19}{res}{space 2}-1.244359{col 31}{space 2} .2609117{col 59}{space 4}-1.755736{col 72}{space 3}-.7329813
{txt}{space 12}/cut3 {c |}{col 19}{res}{space 2}-.2215274{col 31}{space 2} .2480795{col 59}{space 4}-.7077543{col 72}{space 3} .2646996
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\mjoshi2\Box\2024 Research\Colombia Data & Research\Research\Gender Research\GenderAnalysis\Data\PSJ Data\PSJ Stipulation.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}26 Nov 2024, 11:06:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}